In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for further long-run equilibrium relations with possibly different persistence levels. The first step consists in estimating the...

A vector autoregressive model allowing for unit roots as well as explosive characteristic roots is developed. The Granger-Johansen representation shows that this results in processes with two common features: a random walk and an explosively growing process. Co-integrating and co-explosive...

A vector autoregressive model allowing for unit roots as well as explosive characteristic roots is developed. The Granger-Johansen representation shows that this results in processes with two common features: a random walk and an explosively growing process. Co-integrating and co-explosive...

We derive the parameter restrictions that a standard equity market model implies for a bivariate vector autoregression for stock prices and dividends, and we show how to test these restrictions using likelihood ratio tests. The restrictions, which imply that stock returns are unpredictable, are...